This study explores improvements in the estimation of snow water equivalent (SWE) over snow-covered terrain using an ensemble-based data assimilation (DA) framework. The NASA Catchment land surface model is used as the prognostic model in the assimilation of AMSR-E passive microwave (PMW) brightness temperature spectral differences ($\Delta$$T_b$) where support vector machine (SVM) regression is employed as the observation operator. A series of synthetic twin experiments are conducted using different precipitation boundary conditions. The results show, at times, DA degrades modeled SWE estimates (compared to the land surface model without assimilation) over complex terrain. To mitigate this degradation, a physically-constrained approach using different $\Delta$$T_b$ for shallow-to-medium or medium-to-deep snow conditions along with a â\euroœdata-thinningâ\euro strategy are explored. Overall, both strategies improve the model ability to encapsulate more of the evaluation data and mitigate model ensemble collapse. The physically-constrained DA and 3-day thinning DA strategies show marginal improvements of basin-averaged SWE in terms of reduction of bias from $10$ mm (baseline DA) to $-5.2$ mm and $-$2.5 mm, respectively. When the estimated forcings are greater than the truth, the baseline DA, physically-constrained DA, and 3-day thinning DA improve SWE the most with approximately 30\%, 31\%, and 24\% reduction of RMSE (relative to OL), respectively. Overall, these results highlight the limited utility of PMW $\Delta$$T_b$ observations in the estimation of snow in complex terrain, but do demonstrate that a physically-based constraint approach and data thinning strategy can add more utility to the $\Delta$$T_b$ observations in the estimation of SWE.

This first paper of the two-part series focuses on demonstrating the predictability of a hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01 degree (∼ 1-km) and 0.25 degree (∼ 25-km). The assessment is conducted via comparisons against ground-based observations and satellite-derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground-based measurements, the superiority of the 0.01 degree estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper-resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse-resolution estimates. The model forced by downscaled forcings in its entirety yields the highest predictability skill in model output states as well as precipitation, which improves the skill obtained by coarse-resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper-resolution versus coarse-resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper-resolution precipitation products to drive model simulations.